geological map generation using aster

www.elsevier.com/locate/rse

Remote Sensing of Environm

Seamless geological map generation using ASTER in the

Broken Hill-Curnamona province of Australia

R.D. Hewson a,*, T.J. Cudahy a, S. Mizuhiko b, K. Ueda c, A.J. Mauger d

aCSIRO Exploration and Mining, ARRC, Western AustraliabERSDAC, Tokyo, Japan

cSumiko Consultants Co., Tokyo, JapandPIRSA Geological Survey of SA, South Australia

Received 20 September 2004; received in revised form 11 April 2005; accepted 28 April 2005

Abstract

The availability of multiple ASTER image acquisitions enables regional-scale geological mapping, though instrument, irradiance,

atmospheric and surface scattering effects can cause problems in generating seamless mosaics of geological information products. These

issues, including shortwave infrared (SWIR) crosstalk, were addressed in producing seamless ASTER geological maps over the Curnamona

Province, associated with the world class Pb–Zn–Ag Broken Hill deposit. Over 35 ASTER scenes covering an area of approximately 52,000

km2 from 14 different overpass dates were acquired. Maps of Al–OH and Mg–OH/carbonate were generated from ASTER SWIR data as

well as a map of quartz content from the thermal infrared (TIR) data. Maps of ferrous iron content were also generated from the SWIR data of

individual ASTER scenes. The SWIR bands also enabled qualitative mapping of the Al–OH composition though garnet and feldspar – rich

units were not well mapped using the TIR. Field sampling and spectral measurements, together with detailed 1 :25,000 mapping and large-

scale HyMap surveying, constrained the accuracy of the ASTER-derived geological products.

Crown Copyright D 2005 Published by Elsevier Inc. All rights reserved.

Keywords: ASTER; Geological mapping; Broken Hill; Curnamona; Multispectral

1. Introduction

The accessibility of inexpensive, satellite-borne, multi-

spectral ASTER data has created new opportunities for the

regional mapping of geological structure and rock types

including alteration products, and regolith. These data have

been used enthusiastically by the minerals industry around

the world. The ASTER sensor was developed by Japan and

launched onboard NASA’s Terra satellite platform. ASTER

acquires imagery within a 60�60 km scene area from 14

different spectral bands with a pixel resolution of between

15 to 90 m, depending on wavelength (Fujisada et al., 1998;

Thome et al., 1998; Yamaguchi et al., 2001). Of particular

interest for remote sensing geoscientists are the inclusion in

ASTER of detectors covering the visible-near infreared

0034-4257/$ - see front matter. Crown Copyright D 2005 Published by Elsevier

doi:10.1016/j.rse.2005.04.025

* Corresponding author. Tel.: +61 8 64368 689; fax: +61 8 64368 555.

E-mail address: [email protected] (R.D. Hewson).

(VNIR), shortwave infrared (SWIR) and thermal infrared

(TIR) wavelength regions offering the potential for discrim-

inating phyllosillicates and also other silicates. Several

examples of generating mineralogical maps using single

ASTER scenes have proved successful (Rowan & Mars,

2003; Hewson et al., 2001).

The area of study encompasses approximately 52,000

km2 of the Curnamona Province from Broken Hill in

western New South Wales to Olary in South Australia and

the surrounding regolith-dominated terrain (Fig. 1). This

study examined the pre-processing issues involved with

handling over 30 ASTER scenes acquired on 14 different

dates within the Curnamona Province (Fig. 1). These pre-

processing issues included SWIR crosstalk (Iwasaki et al.,

2001), which has a significant detrimental effect on SWIR

spectral signatures. Following pre-processing, a number of

quality-control issues for the ASTER-derived geological

maps were also examined with the aid of field measure-

ent 99 (2005) 159 – 172

Inc. All rights reserved.

139° E 140° E 141° E 142° E 143° E

31° S

32° S

33° S

NTQld

NSW

SA

Vic

Tas

WA

Fig. 1. ASTER scenes acquired from fourteen different dates (each colour corresponding to different acquisitions). Blue boundary marks project study area.

(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172160

ments and airborne hyperspectral HyMap data (Robson et

al., 2003).

The main objective of this study was to generate new,

accurate and seamless geological/mineralogical information

from ASTER images acquired within the Curnamona

Province. Specific objectives include:

1. Characterising of the SWIR crosstalk effect and assess-

ment/development of methods that can effectively

remove this instrument problem;

2. Characterising of the effects of the atmosphere, espe-

cially water vapor, in the SWIR;

3. Characterising of cloud/cloud shadows and strategies for

their masking;

4. Generating a method for generating seamless geological

products;

5. Identifying the diagnostic spectral features that can be

targeted for mapping mineral groups within outcropping

geological units and regolith of the Curnamona Province;

6. Devising suitable algorithms for mapping such mineral

groups over multiple mosaiced ASTER scenes;

7. Validating the derived information products using field/

airborne data and scene-based methods;

8. Establishing the credibility or otherwise of the generated

maps by comparison with published geology from the

New South Wales and South Australian Geological

Surveys in conjunction with field observations and

spectral measurements;

9. Contributing more geological mapping detail to the Olary

Domain of the Curnamona Province.

2. Geological setting

The study area for this project encompasses the

Curnamona Province that continues to attract interest for

its potential economic Broken Hill-style deposits of Pb–

Zn base metals and possible Cu–Au systems associated

with hydrothermal alteration. The world-class Broken Hill

Pb–Zn–Ag orebody within the eastern part of the study

area is associated with high-grade metamorphic rocks. A

variety of tightly folded and high-grade metamorphic units

form well-exposed outcrops, including gneiss, schist,

pelite, psammite, amphibolite and granulite lithologies

(Stevens et al., 1988). An equivalent suite of classified

geological units belonging to the Curnamona Province is

also found in the Olary Domain of South Australia

(Conor & Fanning, 2001) although this area lacks the

detailed 1 :25,000 geological mapping of the Broken Hill

Domain.

Of interest for exploration in the Broken Hill area is the

mapping of regional prograde and retrograde metamor-

phism and possible metasomatic alteration, either associ-

ated associated with the Broken Hill type syngenetic or

epigenetic fluids. Previous studies have shown retrograde

alteration associated with the development of muscovite/

sericite and chlorite (Corbet & Phillips, 1981). Rowan and

Mars (2003) have shown that ASTER can map AlOH

abundance and possibly changes related to AlOH chem-

istry (Duke, 1994), associated with metamorphism.

3. Pre-processing issues and strategies for mosaicing

ASTER imagery

The generation of multi-scene ASTER image ‘‘seam-

less’’ products requires consideration of sensor character-

istics (i.e. crosstalk), atmospheric effects (scattering and

transmission), soil conditions and anthropogenic effects.

Chief amongst these issues for the ASTER SWIR bands are

instrumental crosstalk effects and atmospheric transmission.

In this study for the most part, level 1B (L1B) ASTER

R.D. Hewson et al. / Remote Sensing of Environment 99 (2005) 159–172 161

radiance-at-sensor images were used for the SWIR bands 4

to 9, because of the possibility of inaccurate atmospheric

correction in the surface reflectance standard product,

AST_07, available from the Land Processes Distributed

Active Archive Centre (LPDAAC) (Rowan & Mars, 2003).

Most importantly, however, at the time of this study the

SWIR crosstalk software correction was not routinely

applied to L1B archived data obtained from either the

Earth Remote Sensing Data Applications Centre (ERS-

DAC) or LPDAAC. Version 3.0 beta SWIR crosstalk

software, courtesy of ERSDAC, was applied to all L1B

data acquired for this study. The Level 2 (L2) ASTER

surface emissivity data used in this study were derived

from the L1B radiance data after atmospheric correction

(Thome et al., 1998) and separation of the emissivity

component from the kinetic temperature component (Gil-

lespie et al., 1998).

3.1. SWIR Pre-processing

SWIR crosstalk is an offset or additive error in radiance

due to the leakage of photons from one detector element to

another (Iwasaki et al., 2001). This cross-detector leakage is

most pronounced from band 4 to bands 5 and 9, but it

affects all SWIR bands. For very dark pixels adjacent to

bright pixels, the crosstalk effect will approach 100% of the

input radiance signal. A spatially dependent software

correction for crosstalk has been developed by Iwasaki et

al. (2001) and has since been incorporated by the Japanese

ASTER Ground Data System (GDS) as a part of its L1B

pre-processing. GDS have made this software publicly

available for users to correct their existing ASTER L1B

data of crosstalk effects (http://www.gds.aster.ersdac.or.jp/

gds_www2002/service_e/u.tools_e/set_u.tool_ecross.html).

This software was applied to the Curnamona L1B data to

generate corrected ASTER Hierarchal Data Files (HDF)

files using input parameters listed in Table 1. These input

parameters include the amplitude, a, of the amount of

incident light leaked from band 4 (% units); jx , the size

(pixels) of the applied Gaussian filter function in the across

track direction; and jy , the size (pixels) of the Gaussian

filter function in the along track direction.

An apparent east-west offset between the different

georeferenced SWIR bands along the scene boundaries

results from the inclined descending orbital path and the

Table 1

Input parameters for ERSDAC Crosstalk Software (v. 3.0) applied in

Curnamona Study

ASTER Band a rx ry

5 0.15 42 39

6 0.06 31 40

7 0.04 29 30

8 0.06 31 40

9 0.15 42 39

staggered timing of acquisition for each of the spectral

bands for a given row/pixel. This offset of the SWIR

image boundaries can be of the order of 20 pixels (i.e.,

600 m) although there is no apparent corresponding

spatial offset between bands within the actual image.

Such offsets between SWIR images can generate artifacts

along the east-west boundaries of mosaicked band ratio

images. Corrections were applied to each ASTER scene of

SWIR images using simple conditional algorithms to test

for the presence of the full complement of pixel spectral

data.

ASTER’s default projection datum, WGS 84, is effec-

tively identical to the Australian datum, GDA94, used for

geological mapping and results presented in this study. A

comparison of the ASTER L1B SWIR data to the 1 :25,000

mapping available in the Broken Hill area, indicated that

the image data spatial accuracy was better than 100 m. An

analysis of the spatial accuracy for seven ASTER scenes

(SWIR bands) acquired at four different pointing angles of

�8.58-, �2.87-,+2.88- and +8.57-, revealed average

residual error distances of 45.2 m, 70.8 m, 34.3 m and

98.4 m, respectively. The overall geometric accuracy from

25 ground control points over the seven ASTER scenes

was 63.7 m and regarded as sufficiently accurate for this

study.

To maximise the dynamic range of the 8-bit SWIR data

and process the L1B data into calibrated radiance at the

sensor (W/m2/sr/Am), a set of gains (unit conversion

coefficients) were applied after the crosstalk correction

(Abrams et al., 2002). The calibration to spectral radiance

units of the L1B ASTER data were then obtained using the

equation, Radiance=(DN – 1) * Gain.

Variable illumination conditions of the ASTER radiance

data resulting from different solar angles can also be a

significant seasonal effect related to the cosine of the solar

incident angle (Schowengerdt, 1997). At the latitude of the

Curnamona study area (¨32- S) and for the ASTER

acquisition time (¨10:30 a.m.), the range of solar incidence

angles for flat ground varies from 60 to 23 degrees (from

winter to summer solstice), producing almost a twofold

change in spectral irradiance. However this effect is

cancelled if spectral normalisation (e.g., band ratios) is

employed in the information-extraction strategy (Abrams et

al., 1983).

Another major issue involved with processing mo-

saicked ASTER SWIR data into seamless maps is the

variability of atmospheric water vapor between different

acquisitions. The lack of ASTER spectral bands over key

water absorption bands and the situation where atmospheric

information from other Terra atmosphere instruments were

not used routinely for ASTER corrections at the time of

this study, means that standard climate models have been

used for ASTER atmospheric corrections. Thus towards the

deep atmospheric absorptions at wavelengths longer than

2.5 Am, L2 surface reflectance data are prone to errors,

particularly for ASTER band 9 (e.g. 2.360–2.430 Am).

http://www.gds.aster.ersdac.or.jp/gds_www2002/service_e/u.tools_e/set_u.tool_ecross.html

Fig. 2. MODTRAN 4 atmospheric radiative transfer model results at

ASTER SWIR spectral resolution (bands 4 to 9) indicating effects of

variable climates and associated water vapor on radiation.


The variation of atmospheric transmissivity at ASTER

SWIR spectral resolution was estimated, using MOD-

TRAN4 (Berk et al., 1999). This atmospheric modeling

indicated that radiance measurements by ASTER bands 4

and 7 are insensitive to changes in water vapor associated

with different modeled climatic conditions (Fig. 2). Bands

8 and 9 are the most affected by water vapor absorption

(Fig. 2). The effects of the changes in the atmospheric

water vapor during different ASTER L1B data acquis-

itions from the Curnamona region become obvious for

mosaicked ASTER band ratios b4 /b7 and b7 /b9 using

L1B data within the Broken Hill region (Fig. 3a,b,c). Fig.

3a shows the different acquisitions of ASTER at different

dates (i.e., different shades). A mosaic of ASTER

radiance data as a simple ratio of bands 4 and 7 yields

Fig. 3. a) ASTER acquisitions for seven different dates for the eastern Broken Hill

b7. Broken Hill Domain outcrop in red. c) ASTER L1b band ratio b7 /b9. Broken H

colour in this figure legend, the reader is referred to the web version of this artic

a seamless mosaic of overlapping imagery (Fig. 3b). By

comparison, the ratio of bands 7 and 9 show major

differences across different acquisition dates (Fig. 3c).

Although path radiance is predominantly occurring within

VNIR wavelengths, any significant additive aerosol

scattering effect within the SWIR would likely to show

up within the b4 /b7 mosaic. The importance of these

results is that differences between ASTER_s SWIR

radiance data acquired at different dates, under varying

atmospheric and solar illumination conditions, appear

effectively multiplicative in nature. In this study ASTER

L1B radiance data were mosaicked assuming linear gain

factors to adjust for variable acquisition conditions.

Further MODTRAN modeling in the future could usefully

also be undertaken to derive aerosol scattering effects and

path radiance. If ASTER SWIR responses to variable

atmospheric conditions are predominantly multiplicative,

path radiance and atmospheric scattering effects should be

near zero.

The significance of the combined errors associated with

crosstalk and inaccurate atmospheric correction can be

demonstrated by the comparison between the ASTER L2

surface reflectance signature and ASD (Analytical Spectral

Devices Inc.) VNIR–SWIR spectral measurements, col-

lected at the Broken Hill Airport and resampled to ASTER

spectral resolution (Fig. 4). The large contrast between the

field reflectance measurements of the gravel and bitumen

runways is not reproduced by the ASTER L2 data (Fig.

4). In addition, the shape of the ASTER SWIR signatures,

portion of the Curnamona study area (blue). b) ASTER L1b band ratio b4 /

ill Domain outcrop boundary in red. (For interpretation of the references to

le.)

Fig. 4. Comparison between ASD field VNIR-SWIR measurements and ASTER L2 surface reflectances for Airfield 1 and Airfield 2 validation sites at

Broken Hill.


particularly at bands 5 and 9, shows a significant

difference compared with the field measurements, due

largely to the uncorrected crosstalk effect (Iwasaki et al.,

2001). These results emphasize the importance of correct-

ing crosstalk in ASTER SWIR radiance data.

Gain factors used for mosaicking Level 1B SWIR

radiance-at-sensor data in this study, were empirically

derived from the band means of overlapping ASTER scene

areas acquired during different satellite orbits. For most

scenes, band means were derived automatically from

overlapping areas using image-derived statistics. However,

areas with clouds and associated shadows required manual

definition of the overlapping scene areas to extract reliable

band-mean statistics. Gains were subsequently calculated

relative to a chosen reference image (e.g., Broken Hill

ASTER scene) from the ratio of band means and applied to

each scene acquired from the same orbit and date. This

simple method effectively yielded seamless images across

14 different ASTER acquisition dates for each SWIR band,

having adjusted the L1B data into ‘‘apparent’’ radiance units

relative to a reference scene. The same set of SWIR gains

could generally be used for all ASTER scenes collected

along the same orbit for most cases, although an exception

was observed for ASTER acquisition straddling the Barrier

Ranges at Broken Hill with likely localized water vapor

variations.

Clouds represent a potential problem for seamless

geological mapping for several reasons. Firstly, both

clouds and their shadows can obscure the underlying

surface. Secondly, clouds can affect those mosaicking

procedures that rely on scene statistics. Hence, clouds

should first be identified and then masked out. It was also

observed that residual crosstalk effects could still be

present in high- and low-albedo areas, including those

associated with clouds, and especially within their shadows

(Fig. 5a–e). These residual effects produced ‘‘false

anomalies’’ in SWIR ratio image products. Although the

ASTER operator, GDS, uses an automatic cloud identi-

fication algorithm that attempts to screen all Level 1A

scenes with greater than 20% cloud cover, this process is

still undergoing improvements and can sometimes be

problematic in areas of limited outcrop, such as in the

Curnamona Province.

An algorithm for masking clouds and their shadows

was developed in this study using thresholded ASTER

L1B bands 10 and 3 radiance data, respectively. The low

albedo observed from cloud shadows in VNIR band 3

images, and the low radiant temperatures of thick cloud

tops observed from TIR band 10 images, enabled the

successful mask development of this ASTER data. It was

found, however, that manual rather than automated histo-

gram thresholding was required to limit the masking to

cloud-related features instead of possible geological or

topographic-related effects (i.e., shadowed areas from

sharp relief). In the example shown, band 3 and band 10

(Fig. 5a,b) were thresholded to produce a mask (Fig. 5c).

Crosstalk-corrected L1B SWIR images were processed to

generate AlOH (including muscovite) abundance maps

using band combination [(b5+b7)/b6] (Rowan & Mars,

2003); however shadowed areas are falsely highlighting

high AlOH content (Fig. 5d). This is predominantly the

result of uncorrected residual crosstalk additively contri-

buting to the ASTER signal. Application of the cloud

mask (Fig. 5c) to the AlOH abundance image produces an

improved map result, removing most of the cloud-induced

artifacts but still highlighting small outcrops in the extreme

south east of the scene (Fig. 5e).

3.2. TIR Pre-processing

A comparison between ASTER emissivity data and the

average of TIR spectral field measurements using Design

Fig. 5. a) ASTER L1B band 3 highlighting cloud and associated shadow; b) ASTER L1B thermal band 10 highlighting cloud; c) cloud mask generated from

thresholded bands 3 and 10; d) ASTER L1B generated AlOH abundance anomalies. Light areas indicate interpreted high AlOH abundance; e) ASTER L1B

generated AlOH abundance imagery masked for clouds and associated shadows. Note the interpreted AlOH anomalies that were associated in Fig. 5d with

cloud covered areas.


and Prototypes’s microFTIR 101 (Hook & Kahle, 1996),

resampled to ASTER spectral resolution, showed similar

signatures for several validation sites including the currently

unused North Broken Hill Mine dump (Fig. 6a). The

consistency of the ASTER-derived surface emissivity

signatures for different acquisition dates (i.e., acquired at

different temperatures and/or atmospheric conditions) was

also examined using two ASTER overpasses (Fig. 6b).

Overall the ASTER derived emissivities, under different

conditions, essentially showed similar signatures though

small spectral variations can be observed in detail,

especially in band 14 (¨11.3 Am) (Fig. 6b).

Initial attempts at generating seamless, accurate, geo-

logical information products derived from ASTER L2 TIR

surface emissivity data yielded images that showed no

apparent residual temperature. Some discontinuous line-

striping, related to systematic drift in instrument response,

was apparent; however the minor nature of this problem,

despite the relatively low signal to noise of these TIR

data, indicated that the final geological products were not

severely compromised by this problem.

4. Strategies for generating ASTER seamless geologic

maps

An overview of the various pre-processing steps devised

for this study dealing with the issues described above is

illustrated in Fig. 7. These steps are time-consuming and

automation of this methodology is needed, particularly

Fig. 6. a) Comparison between field mFTIR and ASTER L2 emissivity signatures at Broken Hill Pit Dump, b) repeatability of ASTER L2 surface emissivity

signatures for two different acquisitions (solid vs dashed lines) at Broken Hill.


involving the calculation of gain factors for the adjustment

of variable illumination and atmospheric conditions between

different acquisitions.

5. Mineral group spectroscopy at ASTER spectral

resolution

The ability of laboratory-based VNIR, SWIR and TIR

spectroscopy to measure and enable identification of

minerals and mineral groups has already been established

for several decades (Hunt & Ashley, 1979; Lyon & Burns,

1963; Vincent et al., 1975; Vincent & Thomson, 1972). In

particular OH-bearing minerals and other silicates have

been shown to display diagnostic spectral features within

the SWIR and TIR wavelength regions, respectively (Clark

et al., 1990; Grove et al., 1992; Salisbury & D’Aria,

1992). Resampling of VNIR-SWIR and TIR mineral

library spectra (Clark et al., 1990; Grove et al., 1992,

Salisbury & D’Aria, 1992) to ASTER spectral resolution

provides a basis for understanding the potential limit of

extracting mineral (group) information from ASTER (Fig.

8a and b). In particular, MgOH minerals such as chlorite

and hornblende, have limited diagnostic SWIR spectral

Fig. 7. Overview of pre-processing strategies for mosaicking ASTER

imagery at Curnamona.

absorption features at ASTER resolution that can be

potentially confused with carbonate (Fig. 8a). Garnets

(e.g., almandine, spessartine) are common high-grade

metamorphic minerals within certain units at Broken Hill

are important indicators of Broken Hill-style base metal

mineralisation (Spry & Wonder, 1988). Garnets display

broad VNIR and TIR features at ASTER spectral

resolution, especially from bands 3 to 4 and 12 to 13,

though possible confusion with the spectral features of

green vegetation at VNIR wavelengths and some mafic

silicates at TIR wavelengths needs to be considered (Fig.

8a,b). Feldspars (e.g., albite, anorthite) are common and

important for indicating alteration associated with albitisa-

tion in the Curnamona Province. However, the lack of a

spectral band in the 9.6 Am region, because of strong

atmospheric ozone absorption experienced from a satellite

platform, renders feldspar mapping difficult at ASTER

spectral resolution.

The modeled ASTER SWIR spectra of AlOH minerals

(e.g., kaolinite, Al-poor and Al-rich mica) displayed in Fig.

8a indicate changes in the symmetry of the AlOH

absorption feature centered at 2.2 Am, or ASTER band

6. Previous work by Duke (1994) has shown that white

mica chemistry (e.g., muscovite/illite, phengite), particu-

larly its Al content, can be inferred by the wavelength of

its 2.2 Am absorption feature. Duke (1994) showed that Al

poor micas (e.g., phengite) display a AlOH absorption

feature with a longer wavelength than Al-rich micas. On

the basis of this absorption feature, observed by ASTER

bands 5, 6 and 7, an estimate of AlOH abundance was

estimated by the ASTER band combination, (b5+b7)/b6

(Rowan & Mars, 2003), and inferred white mica compo-

sition by band ratio indexes b5/b6, b7/b6 and b7/b5.

Resampling of muscovite library spectra into ASTER

equivalent spectra suggested that a high b5/b6 and low

b7/b6 can represent a longer wavelength mica absorption

feature compared to the converse situation of a low b5/b6

Fig. 8. a) Laboratory (solid lines) and ASTER equivalent resampled (dashed) VNIR–SWIR mineral library reflectance signatures; b) laboratory and ASTER

equivalent resampled TIR mineral library emissivity signatures.


and high b7/b6 result. It is also suggested by these

resampled spectra, that kaolinite may be discriminated

from white mica using b7/b5. In a similar way, abundances

of MgOH (e.g., chlorite, hornblende) and carbonate (e.g.,

calcite) group minerals were estimated by the ASTER

band parameter, (b6+b9)/b8, based on their 2.33–2.35 Am(band 8) absorption feature. The presence of ferrous iron in

MgOH silicates can also display a steady rise in the SWIR

spectral reflectance signature approximately from 1.0 to

2.0 Am. This trend can be preserved at ASTER spectral

resolution as indicated for the chlorite and hornblende

spectra (Fig. 8a). Estimates of the ferrous iron content

using the ASTER band ratio b5 /b4 were also examined in

this study.

Quartz has a pronounced reststrahlen TIR spectral feature

within the 8 to 9.2 Am region producing a diagnostic

emissivity signature that can be also observed by ASTER

bands 10 to 12 (Fig. 8b). Phyllosilicates (e.g., muscovite,

kaolinite) by comparison generally display longer TIR

wavelength spectral features between 8.6 to 9.6 Am or

ASTER bands 11 and 12. Consequently in this study

ASTER band ratio b13 /b10 was used to map quartz-rich

units and regolith (Fig. 8b).

6. ASTER seamless geologic maps for the Curnamona

Province

Seamless images were generated from the ASTER L1B

SWIR radiance data to map major geological units rich in

Al–OH, Mg–OH/carbonate and quartz abundances (Fig.

9a–d). In these images, brighter areas represent surface

materials with deeper absorption features which are

assumed to be associated with higher abundances of AlOH,

MgOH-carbonate and quartz. In particular, the brightest

areas highlighted in Fig. 9b correlate with the mica-rich

outcrops and associated colluvium within the Broken Hill

and Olary Domains (‘‘A’’ and ‘‘B’’, respectively) while

quartz-rich areas tend to be associated with alluvial outwash

and accumulations within the Lake Frome Basin (‘‘C’’) (Fig.

9d). The MgOH-carbonate abundance image product high-

lights Adelaidean carbonate-rich units south of the Olary

Domain (‘‘D’’) and to a lesser extent, amphibolite-rich units

within the Broken Hill Domain (Fig. 9c).

Comparisons between HyMap (Robson et al., 2002) and

ASTER data for the AlOH abundance image, [(b5+b7)/b6]

in the Broken Hill Domain (Area I and Area II, Fig. 9a),

show the accuracy of the mosaicked ASTER SWIR data

product (Fig. 10a,b). The ASTER and HyMap derived

AlOH abundance (inverted) maps both show that the

northerly and northeasterly AlOH-rich units (dark areas)

decrease in white mica abundance towards the southwest

(Fig. 10a, b). There is also a correspondence between the

ASTER derived AlOH abundance image and the radio-

metric potassium concentration obtained from airborne

geophysical surveying (Fig. 10c) (Robson & Spencer,

1997). No explanation is available as yet for these spatial

patterns of K-mica abundance although it appears to be

associated with trends also associated with metamorphic

retrograde alteration at Broken Hill (Corbet & Phillips,

1981). It is also interesting to note that the Broken Hill type

deposits are located in areas relatively poor in muscovite.

a) b)

c) d)

III

III

IVA

B

C

D

Fig. 9. a) Study area for the ASTER Curnamona Project (cyan) showing the geological outcrops (grey) (AGSO, 2000) and 1 :250,000 map sheets

encompassing the Curnamona Province; b) ASTER derived AlOH abundance imagery; c) ASTER derived MgOH-carbonate abundance imagery; d) ASTER-

derived quartz abundance imagery. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 10. a) ASTER derived AlOH abundance within the Broken Hill Domain and surrounding regolith areas (Area I—Fig. 9 a) Broken Hill Mine indicated by

‘‘X’’ ; b) HyMap derived AlOH abundance; c) Airborne geophysics derived radiometric potassium concentration.



Further investigation is required to investigate its relevance

for the alteration history of the Broken Hill deposit.

The ability of ASTER band ratio b5 / b4 to map

lithologies rich in ferrous iron silicates was examined and

Fig. 11. a) Olary Domain geological outcrop boundaries (Area II—Fig. 9 a) (PI

derived ferrous iron silicate index; d) MNF bands 1, 2 and 3 of ASTER TIR su

emissivity data.

studied in Area III (Figs. 9a and 11a) within the units of the

Olary Domain (Fig. 11c) containing several MgOH/

carbonate anomalies (Fig. 11b). In particular amphibolite/

calcalbite units (396000E, 6434800N; dark green Fig. 11a)

RSA, 2000); b) ASTER-derived MgOH/carbonate abundance; c) ASTER-

rface emissivity data; e) MNF bands 2, 3, and 4 of ASTER TIR surface


are discriminated by both the ferrous iron silicate (Fig. 11c)

and the MgOH/carbonate image (Fig. 11b) products. Using

both of these products appears to offer the potential to

discriminate between MgOH group minerals and some

carbonate minerals. In this region, the amphibolite-rich

units at 399500E, 6437200N are highlighted in both image

products (Fig. 11b,c), whereas the Skillogalee and Auburn

Dolomites, within the Burra Group (401800E, 6437600N;

burgundy, Fig. 11a), are highlighted only by the MgOH/

carbonate product (Fig. 11b). Note that this interpretation

and discrimination could be complicated by ferrous-bearing

carbonates (e.g., ankerite, siderite and ferroan dolomite) in

other geological settings. Attempts at generating ASTER-

derived mosaics, representing ferrous iron content across

the Curnamona, proved problematic and showed significant

differences between image acquisition dates. This is likely

to be the result of residual crosstalk contributions for bands

4 and 5 used in the ratio product b5 /b4. In particular this

product would be more sensitive to crosstalk, reducing

band 4 and increasing band 5 in a non-linear manner

related to average radiance levels present at each acquis-

ition. Attempts were also made to discriminate these

MgOH and carbonate units using the Minimum Noise

Fig. 12. a) Geology of the Broken Hill Domain–Mundi Mundi Escarpment (Are

AlOH composition; d) HyMap-derived AlOH wavelength index from short (blue:

signatures. (For interpretation of the references to colour in this figure legend, th

Fraction transformation (Green et al., 1988) applied to the

L2 surface emissivity TIR product. However the emissivity

data proved noisy with limited dimensionality and pro-

duced no clear discrimination of the carbonate and MgOH-

rich units (Fig. 11d,e).

A comparison between the AlOH absorption wavelength

measured using the hyperspectral airborne data (Fig. 12d)

and the ASTER RGB band ratio AlOH imagery (Fig. 12c)

was undertaken along the north-western margins of the

Broken Hill Domain (Area IV, Figs. 9a and 12a). The area

straddles the Mundi Mundi fault line and associated

escarpment where an apron of alluvium and colluvium

flanks the uplifted Broken Hill Domain to the southeast

(Fig. 12a). Spectral profiles of the hyperspectral HyMap

imagery reveals that areas of blue (‘‘A’’), green (‘‘B’’) and

red (‘‘C’’) shown in Fig. 12d correspond to a measurable

increase in the wavelength of the main AlOH spectral

feature (Fig. 12e). The ASTER RGB AlOH image on the

other hand shows a qualitative similarity only to the HyMap

results (Fig. 12c). Also in some areas mapped by the

HyMap data as AlOH poor (e.g., ‘‘D’’, Fig. 12d), the

processed ASTER results wrongly highlighted areas of

high-abundance Al-poor mica (Fig. 12b,c).

a III—Fig. 10 a); b) ASTER derived AlOH abundance; c) ASTER derived

2.195 Am) to long (red: 2.207 Am) wavelength; e) HyMap SWIR spectral

e reader is referred to the web version of this article.)


A series of field transects were conducted as part of the

validation of ASTER image products at Broken Hill. These

transects involved a series of closely spaced measurements

at 1-m intervals for approximately 150 m across the

geological strike of contrasting Broken Hill outcropping

units. Spectral reflectance measurements of soil and rock

outcrop were measured along these transects with the ASD

Fieldspec Pro VNIR-SWIR spectrometer to compare with

ASTER spectral signatures. Along one of the transects,

ASD field measurements and samples were collected across

several narrow (~20–50 m) amphibolite, retrograde schist

and psammite units (Fig. 13a). The ASD field spectral

Fig. 13. a) Darling Creek field traverse with geology (Area IV—Fig. 9 a) and AS

acquired from traverse; c) HyMap SWIR sample signatures corresponding to traver

Am); e) AlOH composition interpreted from ASTER SWIR log residuals; f) Ca

intervals along field traverse listed. (For interpretation of the references to colour i

measurements (Fig. 13b) were shown to compare favour-

ably with the HyMap image signatures (Fig. 13c) within the

SWIR wavelength region. The ASD and HyMap signatures

corresponding to retrograde shear schists, amphibolites, and

psammite units are highlighted by grey, green and blue

spectra respectively (Fig. 13b and c). Comparisons between

the New South Wales Geological Surveys published

1 :25,000 geology (Fig. 13a) and the ASD and HyMap

signatures were reasonable, showing AlOH (i.e., 2.2 Am)

absorption features for the mica-rich schists and psammite/

psammopelite units (Fig. 13b and c). The ASD and HyMap

signatures also identified the MgOH’s spectral feature

D measurements (white squares) along traverse; b) ASD SWIR signatures

se; d) HyMap-derived AlOH wavelength index (blue=2.197 Am, red=2.202

librated ASTER SWIR spectral signatures (*=ASTER band centers) for

n this figure legend, the reader is referred to the web version of this article.)


between 2.3 to 2.33 Am associated with the amphibolite

units. Discrepancies between some of the geological

boundaries and field/image signatures were apparent;

however this may also possibly be a result of inaccuracies

within the 1 :25,000 mapping or the presence of colluvial

float material.

A wavelength index image was generated from the

HyMap data to represent shifts in the wavelength position of

the AlOH 2.2 Am feature (Fig. 13d). This wavelength index

represents possible changes in the white mica chemistry as

suggested by Duke (1994) where blue areas indicate shorter

wavelength (i.e., 2.197 Am) Al-rich mica compared to red

areas representing longer wavelength Al-poor mica (i.e.,

2.202 Am) (Fig. 13d). ASD field measurements along the

transect shown (Fig. 13b) indicate a change for the AlOH

absorption feature from 2.203 Am to 2.199 Am northwards

as suggested by the HyMap derived AlOH wavelength

index image (Fig. 13d). The ASTER derived AlOH

composition RGB imagery was also compared with the

ASD signatures and geology along the Darling Creek

Traverse. However it was clear that the coarser spatial

nature of the SWIR data (i.e. 30 m) limited ASTER’s

discrimination and usefulness for mapping these narrow

geological units (Fig. 13a).

Crosstalk-corrected ASTER L1B radiance data encom-

passing this area were calibrated using ASD field spectral

measurements and processed into log residuals (Green &

Craig, 1985) for comparison with ASD and HyMap

spectra. The resulting ASTER data produced SWIR

reasonable signatures with no obvious distortion of bands

5 and 9 (Fig. 13f). There is the suggestion of a shift to

more left symmetric AlOH absorption feature (i.e., from

red to yellow, Fig. 13e) corresponding to possibly shorter

wavelength mica chemistry north along the traverse (Fig.

13e). However the spectral resolution of ASTER SWIR

bands precludes accurate estimation of the AlOH 2.2 Amabsorption feature’s wavelength (Fig. 13f). These ASTER

transect results and their comparison with field and

hyperspectral data indicate that ASTER has limited

potential to provide compositional information for small

changes in AlOH chemistry as observed in Broken Hill,

even assuming well-calibrated SWIR radiance data.

7. Conclusions

Several pre-processing steps were required to generate

seamless imagery before band combination processing was

applied to target specific mineral absorption features. These

steps included correction for additive SWIR crosstalk

effects, SWIR band image offsets and also gain adjustments

for variable atmospheric/illumination conditions during

different acquisitions. As part of validation studies under-

taken for this study, field spectral measurements revealed

that ASTER’s SWIR crosstalk effect was a significant

factor for the ASTER L2 surface reflectance data, parti-

cularly for bands 5 and 9. Although ERSDAC’s crosstalk

correction software (Version 3.0) alleviated much of the

crosstalk problem for ASTER L1B data, artifacts were still

apparent, especially for areas of low albedo (e.g., cloud

shadows) and but also for areas of high albedo. As a

consequence, masking for clouds (and water) is a critical

pre-processing step.

Atmospheric radiative-transfer modeling revealed that

variable water vapor associated with different climatic

models, is a significant issue for the ASTER L1B SWIR

data, particularly for bands 8 and 9. The effects of variable

atmospheric conditions and from variable solar illumina-

tion conditions appeared to be multiplicative from band

ratio results of different ASTER acquisitions. Successive

mosaicking of ASTER SWIR data was subsequently

derived by the application of gains upon overlapping

ASTER SWIR L1B images from 14 different acquisitions.

The resulting mosaicked ASTER L1B SWIR images were

successfully processed using band ratios to measure the

abundance of mineral groups including AlOH and MgOH/

carbonate within outcropping and/or regolith units of the

Broken Hill-Curnamona Province. ASTER derived AlOH

abundance imagery compared well with large-scale air-

borne hyperspectral HyMap survey results, providing

confidence in the application of multiplicative gains to

adjust ASTER scenes acquired on different dates. During

this study, it was also found that discrimination between

MgOH- and carbonate-rich units was possible using

ASTER if ferrous iron products were generated to assist

the discrimination of MgOH group minerals. Partial

success was also achieved in generating seamless maps

qualitatively representing AlOH composition from ASTER

SWIR images. However, its reliability was significantly

reduced in areas of low albedo and in the presence of

chlorite-rich units. Despite the application of crosstalk

correction software, some residual crosstalk effects still

proved problematic.

ASTER TIR L2 surface emissivity data compared

favourably with field spectral measurements and also

produced reasonably consistent signatures, independent of

acquisition conditions. As a consequence, ASTER emissiv-

ity data were mosaicked and processed to generate quartz-

abundance images and were found to highlight regolith

accumulations of alluvial quartz and some units of quartzites

and sandstones.

Acknowledgement

This work was financially supported and encouraged by

the Geological Survey of South Australia within the

Department of Primary Industries (PIRSA), as part of

PMD*CRC activities. Several individuals, particularly

within PIRSA, played a key part in this project including

Paul Heithersay, and Stuart Robertson and others within the

Curnamona Team. Japan’s ERSDAC provided ASTER data,


crosstalk-correction software, and support during field

validation activities in Broken Hill. LPDAAC of the United

States Geological Survey also provided ASTER data.

Support was also gratefully received from CSIRO’s Glass

Earth Project, its coordinator, Joan Esterle and co-worker,

Joanna Parr. US and Japanese members of the ASTER

Science Team provided technical feedback during this

research. Andrew Rogers modelled the effects of variable

atmospheric conditions upon ASTER data. New South

Wales Department of Mineral Resources and Geoscience

Australia granted permission to publish radiometric data

from the Discovery 2000 geophysical database. Processed

HyMap data of Broken Hill were gratefully received from

Peter Hausknecht of HyVista.

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